Code
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential,Model
from keras.layers import Dropout, Flatten, Dense,Input
from keras import applications
from keras.preprocessing import image
from keras import backend as K
K.set_image_dim_ordering('tf')
# dimensions of our images.
img_width, img_height = 150,150
top_model_weights_path = 'bottleneck_fc_model.h5'
train_data_dir = 'Cats and Dogs Dataset/train'
validation_data_dir = 'Cats and Dogs Dataset/validation'
nb_train_samples = 20000
nb_validation_samples = 5000
epochs = 50
batch_size = 16
input_tensor = Input(shape=(150,150,3))
base_model=applications.VGG16(include_top=False, weights='imagenet',input_tensor=input_tensor)
for layer in base_model.layers:
layer.trainable = False
top_model=Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(256,activation="relu"))
top_model.add(Dropout(0.5))
top_model.add(Dense(1,activation='softmax'))
top_model.load_weights(top_model_weights_path)
model = Model(inputs=base_model.input,outputs=top_model(base_model.output))
datagen = ImageDataGenerator(rescale=1. / 255)
train_data = datagen.flow_from_directory(train_data_dir,target_size=(img_width, img_height),batch_size=batch_size,classes=['dogs', 'cats'],class_mode="binary",shuffle=False)
validation_data = datagen.flow_from_directory(validation_data_dir,target_size=(img_width, img_height),classes=['dogs', 'cats'], batch_size=batch_size,class_mode="binary",shuffle=False)
model.compile(optimizer='adam',loss='binary_crossentropy', metrics=['accuracy'])
model.fit_generator(train_data, steps_per_epoch=nb_train_samples//batch_size, epochs=epochs,validation_data=validation_data, shuffle=False,verbose=
我在猫和狗数据集上实现了图像分类器（https://www.kaggle.com/c/dogs-vs-cats/data https://www.kaggle.com/c/dogs-vs-cats/data）使用 keras（使用 VGG16 网络学习的迁移）。代码运行没有错误，但在大约一半的时期内，准确度停留在 0.0%，一半后，准确度增加到 50%。我正在使用 Atom 和氢气。
我该如何解决这个问题。我真的不认为我对 VGG16 这样的数据集有偏见问题（尽管我对这个领域相对较新）。
